Water-soluble ions and source apportionment of PM2.5 depending on synoptic weather patterns in an urban environment in spring dust season

Emission sources and meteorological conditions are key factors affecting the intensity and duration of air pollution events. In the current study, using the daily concentrations of PM2.5 (particulate matter with a diameter ≤ 2.5 μm) and the water-soluble ions thereof in Lanzhou from March 1, 2021, to May 31, 2021, we investigated the contributions of emission sources and locations of potential sources through positive matrix factorization and potential source contribution function analysis. In addition, synoptic weather patterns affecting pollution were typed using T-model principal component analysis. The results revealed that the average concentrations of PM2.5 for the entire spring, dust storm days, and normal days were 54.3, 158.1 and 33.0 μg/m3, respectively. During dust storm days, sulfate produced from primary emissions was mainly present in the form of K2SO4, Na2SO4, MgSO4, and CaSO4, and nitrate was mainly produced through secondary conversion and took the form of NH4NO3. Dust, industrial entities, biomass combustion, metal smelting, secondary aerosol, and sea salt contributed to 32.0, 29.8, 13.4, 11.2, 10.8 and 2.7% of the spring PM2.5, respectively, in Lanzhou. The main potential sources of PM2.5 during the normal days were in the western parts of Lanzhou. Dust storms entered Lanzhou through the Hexi Corridor from several dust sources: southeastern Kazakhstan, Mongolia, the Kurbantungut Desert, and the Badain Jaran Desert. The northwest high-pressure; northern strong high-pressure and southwest low-pressure; northwest high-pressure and southwest high-pressure synoptic weather circulation types were prone to dust storms. Our results may provide a basis for local environmental governance.

Water-soluble ion analysis. Ultrasonic techniques were used to extract inorganic ions from the samples; typically, more than 98% of sulfate, ammonium, and nitrate ions can be extracted through ultrasonic treatment 23 . A quarter of each filter sample was immersed in a sealed vial containing 50 mL of ultrapure water and was extracted three times with ultrasonic treatment for 15 min. The water-soluble ion content in the PM 2.5 was determined using Metrohm 925 Eco IC (Switzerland) ion chromatography. Anion (F − , Cl − , NO 3 − , and SO 4 2− ) concentrations were determined using a Metrosep C 6-150/4.0 (Metrohm) column with a mixture of 3.2 mMol/L Na 2 CO 3 and 1 mMol/L NaHCO 3 as an eluent. Cation (Na + , NH4 + , K + , Ca 2+ , and Mg 2+ ) concentrations were determined using a Metrosep A Supp 5-150/4.0 (Metrohm) column with 4 mMol/L HNO 3 as an eluent. The detection limits for these ions were in the range of 0.01-0.03 μg/m 3 . The samples of 7 days were selected for repeat testing and the standard error was found to be less than 5%. To evaluate the recovery efficiency of the method, the sample solution was spiked with a known amount of ions and the measurement found that the recovery was 95.6-102.0%.
Positive matrix factorization analysis. A positive matrix factorization (PMF) receptor model was used as a multivariate factor analysis approach to analyze the contribution of different sources to the samples on the basis of the components and speciation of these sources 24 . PMF was used to sort the specific sample data matrix into factor contribution (G) and factor profile (F) matrices 25,26 . The PMF equation is as follows: where i is the number of the samples, j represents the types of chemical species, n is the number of factors, x is the measured concentration, g indicates the amount of mass contributed by the factors, f represents the species profile of the sources, and e ij represents the residual of the species.
In PMF, nonnegativity constraints are used on the matrices to reduce the rotational degrees of freedom 27 ; furthermore, Q is minimized in the analysis process to obtain the optimal solution. The equation for Q is as follows: www.nature.com/scientificreports/ where u ij represents the estimated uncertainty values, MDL is the method detection limit, and RSD is the error fraction. In this study, the RSD was set to 0.1 28 . The water-soluble ion samples were added to the Environmental Protection Agency PMF 5.0 (United States Environmental Protection Agency) for analysis of the contribution of sources. The signal-to-noise (S/N) criterion was used to classify the input variables, and the species Na + , NH 4 + , K + , Ca 2+ , Mg 2+ , Cl − , NO 3 − , and SO 4 2− were classified as "strong variables" and F − as "weak variables" for which a triple uncertainty was used. And the displacement (DISP) method was used to assess the uncertainty in profiles and contribution of the estimated factors. Results showed that the drop of Q value was less than 0.1% and no factor swapped for the smallest dQmax, indicating that the results were credible.
Potential source contribution function analysis. Potential source contribution function (PSCF) analysis is a method for identifying potential source areas of air pollution by using backward trajectory analysis 14 . In this study, reanalysis data with a resolution of 1 × 1° provided by the NCEP Global Data Assimilation System was used as the initial meteorological field. The Hybrid Single-Particle Lagrangian Integrated Trajectory model was used to calculate the backward trajectory of Lanzhou in spring. The PSCF model was based on the ratio of the cumulative probability of pollution trajectories passing through each grid to all trajectories 29 . The equation is as follows:  Classification of synoptic weather patterns. Spring synoptic weather patterns were classified using the T-model principal component analysis (T-PCA) method improved upon by Huth et al. 30 in the PCA aspect. The basic purpose of T-PCA is to sort original high-dimensional data Z into two low-dimensional matrices F and A (Z = FA T , with F as the principal component and A as the load). All principal components were sorted according to their contributions to the original data, and the corresponding principal component F was filtered and dimension reduced on the basis of the cumulative contribution of the original data [30][31][32] . T-PCA has temporal and spatial stability and can accurately reflect the characteristics of the original weather circulation 33 . In this study, regions 80°E to 120°E and 30°N to 60°N were selected for the analysis of prevailing weather circulation. T-PCA was performed using COST733 class software, which was developed through an initiative within the Earth System Science and Environmental Management domain of the European Cooperation in Science and Technology framework (http:// cost7 33. met. no/).

Results and discussion
The average PM 2.5 and water-soluble ion concentrations for whole spring and the dust storm and normal days of March 1 to May 31, 2021, in Lanzhou are listed in  35,36 , respectively. In recent years, Lanzhou has committed to environmental management and implemented measures such as emission reduction, dust reduction and vehicle control to improve air quality 22 . And the reduction in anthropogenic emissions from industry and transportation due to COVID-19 lockdown also led to the reduction of air pollutants in Lanzhou in the spring of 2021 37 . During dust storm days, PM 2.5 concentrations increased rapidly, as well as the concentrations of Na + , Mg 2+ , Ca 2+ , K + , Cl − , and SO 4 2− . Previous studies also showed that PMs and crustal material increased rapidly during dust storm days. A study in Xi'an 38 , northwest China, showed that Ca 2+ , SO 4 2− and NO 3 − were the most abundant ions in TSP samples during dust storm days, but the concentration of secondary aerosols was lower than those during normal days. A study in Riyadh, Saudi Arabia 39 presented that PMs and some elements (such as Fe, Mg, Ca and Al) increased more than doubled during dust storm days. Shen et al. indicated that during dust storms, crustal matter and carboncontaining matter accounted for 69% and 14% of PM 2.5 , respectively 40 . However, the concentration of NO 3 − and NH 4 + decreased during the dust storm days. A study in Lanzhou indicated that due to the comprehensive effects of dust adsorption, the drag effect of sedimentation, and the strengthening of atmospheric diffusion capacity caused by increased wind speeds, concentrations of local emissions have decreased significantly, resulting in    48 . In this study, during normal days, a negative correlation was evident between Na + and Cl − , whereas during the dust storm period, a significant positive correlation was observed between the two ions, indicating that the sandstorm brought sea salt aerosol (or salt water lake aerosol) through long-range transmission to Lanzhou.
The main characteristic species and corresponding contributions of the six emission sources identified using the PMF model are presented in Figs. 3 and 4. Dust was the highest contributor to spring PM 2.5 concentrations in Lanzhou, accounting for 32.0%. The main characteristic water-soluble ions with a dust source were Ca 2+ , Mg 2+ , and SO 4 2− . Lanzhou is located on the northeastern edge of the Tibetan Plateau, with the Taklamakan, Kumtag, and Gurbantunggut deserts to the west and the Badain Jaran and Tengger deserts to the north 49 , leading to frequent dust storms in spring. The second highest emission source contributing to PM 2.5 concentrations was industrial entities, accounting for 29.8%. The main characteristic water-soluble ions with industrial entities source were SO 4 2− , NO 3 − , NH 4 + , and Na + . The volatilized chemical solvents and produced pollutants from large industrial sources (such as power plants, petrochemical plants, oil refineries, and pharmaceutical plants), located in the upper winds of Lanzhou, were transported to the observation area, resulting in a high concentration of industrial emissions 50 . The contribution of biomass combustion to PM 2.5 concentrations was 13.4%, and the main water-soluble ions with this source were K + and Cl − . The contribution of metal smelting to PM 2.5 concentrations was 11.2%, and the main water-soluble ions with this source were F − , SO 4 2− , NO 3 − and Ca 2+ . Lanzhou is a major aluminum production base in China, and the electrolysis of aluminum produces F − , resulting in large amounts of F − from metal smelting sources 51 . The contribution of secondary aerosol to PM 2.5 concentrations was 10.8%, which was mainly characterized by high concentrations of NH 4 + , SO 4 2− and NO 3 − . Lanzhou is located in the Yellow River valley basin, surrounded by mountains 52 . This unique basin topography prevents air pollutants from being easily diffused, and the long-term retention of these pollutants facilitates the formation of secondary aerosols 52,53 . Sea salt (or salt water lake aerosol) contributed the least to PM 2.5 concentrations, only accounting for 2.7%. The main characteristic water-soluble ions with a sea salt source were Na + and K + . In addition, correlation analysis revealed that sea salt aerosol (or salt water lake aerosol) was brought to Lanzhou through the long-range transport of dust storms. Na + , Cl − , K + and Mg 2+ are the main ions of sea salt aerosol 54 . Reaction of gaseous or aqueous HNO 3 or H 2 SO 4 with NaCl in sea salt or oxidation of gaseous SO 2 to sulfuric acid by sea salt droplets leads to chloride depletion in sea salt aerosols 25 . A study in Shenzhen, China also reported that the aged sea salt was indicated by high loadings of Na + and Mg 2+ and low concentration of Cl −56 . A study in Lijiang, China indicated that sea salt aerosol contains 37% of K +56 .  , might be derived from large industrial sources at locations upwind. Yu et al. found that spring fugitive dusts, including soil and road dust, were more than twice as high as in other seasons in Beijing 61 .
The PSCF analysis for dust storm days, normal days, and the entire spring of 2021 is presented in Fig. 5. The main trajectories that contributed most to PM 2.5 concentrations in Lanzhou during the dust storm days are presented in Fig. 5a. The air mass of trajectory 1 originated from the desert in southeastern Kazakhstan and the Gurbantunggut Desert in northern Xinjiang and moved southeastward to Lanzhou. Trajectory 2 moved more eastward than trajectory 1, bringing dust from the deserts of western Mongolia. The air mass of trajectory 3  (Fig. 5b). This may be due to the large amount of pollutants generated in the Xigu District, the largest petrochemical base in western China, which is located upstream of the downtown area 50 . From the spring-wide perspective, the contribution to PM 2.5 from the northern part of Lanzhou and the www.nature.com/scientificreports/ Hexi Corridor was increased (Fig. 5c). Tang et al. indicated that under conditions of strong zonal jet fronts in the midlatitudes of East Asia, the Hexi Corridor is prone to low-level jets, which lead to strong sandstorms in spring 62 . Liang et al. demonstrated that the type of land (especially bare land) on the air mass movement path is a key factor that can determine the intensity of a dust storm 17 . Improving land management in windward areas of Lanzhou, especially in the Hexi Corridor, may reduce the frequency and intensity of dust storms. The transport of pollutants would lead to the variations in concentrations of local pollutants. A study in southern Peninsular Malaysia 63 indicated that there was a high potential for long-range transport of pollutants from heavily polluted areas, which could have a significant impact on air quality in less polluted areas. Chen et al. found that cities in the south and east of Chengdu, such as Chongqing and Neijiang, were the main sources of pollution in Chengdu 64 . Transmission of dust storms could lead to a substantial increase in the concentration of local particulate matter. A study in Seoul, Korea 41 reported that the concentrations of PM increased during dust storm days. Xiong et al. suggested that regional sandstorms transported to Wuhan via northwest air masses in spring lead to 1.1-1.8 fold increase in the concentration of crustal elements such as Al, Ca, and Mg 65 . Turap et al. found that the concentration of PM 2.5 in Xinjiang, China was seriously affected by dust particles from Dushanzi district and Kazakhstan in spring 66 .
The average typical synoptic circulation from March 1, 2021, to May 31, 2021, was determined using T-PCA (Fig. 6). Type 1 was the northwest high pressure (NWH). There was a high pressure ridge in the northwestern part of Lanzhou area, and Lanzhou was affected by the Northwest (NW) wind before the ridge of high pressure. Type 2 was strong northern high pressure and southwest low pressure (NHSL). The north of Lanzhou contained a high-pressure area, and the southwest contained a low-pressure center. The atmosphere converged to the lowpressure area, and northeast winds prevailed in Lanzhou. Type 3 was northwest high pressure and southwest high pressure (NSH). The northwest and southwest of Lanzhou had two high-pressure systems. A strong north wind formed at the front of the northwest high-pressure system and a west wind formed from the southwest highpressure system. Thus, northwest and west winds prevailed in Lanzhou in type 3. Type 4 was southwest strong high pressure (SWH + ). Lanzhou is located in front of a high-pressure system and was, therefore, influenced by high-pressure dispersion, with prevailing west and southwest winds. Type 5 was southwest high pressure and southeast high pressure (WEH). Lanzhou is located behind a southeast high-pressure system with prevailing south winds. The frequencies of typical synoptic circulation types 1, 2, 3, 4 and 5 were 18.2, 21.7, 21.2, 19.8 and 19.0%, respectively. The typical synoptic circulation for types NWH, NHSL, and NSH were prone to dust storms. The frequency of dust storms was lower for the SWH + and WEH types.
The synoptic circulation of the most severe dust storm of the past 10 years (lasting from March 14, 2021, to March 20, 2021), which affected most parts of northern China, is presented in Fig. 7. The synoptic circulation for this dust storm process was mainly of the NSH and NHSL types. The synoptic circulation at UTC 18:00 on March 14, 2021 was of the NWH type. A cold high-pressure center occurred in the west of Mongolia, adjacent to a cyclonic system on its eastern side. A rapid accumulation of sand and dust in southern and western Mongolia and central and western Inner Mongolia was influenced by the tail end of the cyclone and the frontal part of high pressure. This sand and dust were rapidly driven into the north of China by cold air. The synoptic circulation at UTC 18:00 on March 15, 2021, and UTC 12:00 on March 17, 2021, were of the NHSL type. A low-pressure center occurred in the southwest region of Lanzhou. The convergence of the atmosphere to Lanzhou led to strong sandstorms in Lanzhou from March 15 to 17. At UTC 12:00 on March 20, 2021, a low-pressure center was still present in the southwest of Lanzhou, causing the sandstorm to continue longer in Lanzhou than in other places. The dust storm faded away when the center of the high-pressure system moved southeast.
In addition to the source of emissions, the frequency and severity of pollution events are influenced by meteorological conditions 67 . Miao et al. indicated that high-pressure systems in the southeast and east of Beijing block the flow of air to the sea, causing the polluted air of the southern industrial area to move toward Beijing 68 . Xu et al. reported that the L-shaped high synoptic circulation type is the most meteorologically adverse type, and it results in a low-pressure gradient force, weak wind speed, and rapid accumulation of PM 2.5 31 . These weather patterns may induce the movement of atmospheric pollutants and adverse meteorological diffusion conditions, which are the key factors affecting the intensity and duration of air pollution events 13,33. Our study has some limitations. First, we measured the composition of only nine water-soluble ions in PM 2.5 and did not include organic carbon, elemental carbon, or metal. Second, we only analyzed the chemical composition and source of PM 2.5 in the spring dust storm season. In future research, other composition such as organic carbon, elemental carbon, and metal elements should be measured. The influence of weather conditions on the pollution in Lanzhou would be further studied on a multiyear scale. Future studies should employ sensitivity simulation to quantify the influence of different weather conditions on air quality.

Conclusion
In this study, the emission sources and contributions of pollutants and the influence of weather situation on air pollution in the spring of 2021 in Lanzhou were comprehensively analyzed. The sources contributions of PM 2.5 analyzed by PMF were dust (32.0%), industrial entities (29.8%), biomass combustion (13.4%), metal smelting (11.2%), secondary aerosol (10.8%), and sea salt (2.7%), respectively. During dust storm days, the concentration of Mg 2+ , Ca 2+ and SO 4 2− increased sharply. The main sources of dust were the Gobi Desert and deserts southwest and northwest of Lanzhou. The classification of weather types through T-PCA revealed that the NWH, NHSL and NSH synoptic circulation types were prone to dust storms.

Data availability
The datasets used in the current study are available from the corresponding author on reasonable request.